Batch normalization embeddings for deep domain generalization

•We propose to accumulate domain-specific batch normalization statistics accumulated on convolutional layers to map image samples into a latent space where membership to a domain can be measured according to a distance from domain batch population statistics•We propose to use this concept to learn a lightweight ensemble model that shares all parameters excepts the normalization statistics and can generalize better to unseen domains•Compared to previous work, we do not discard domain-specific attributes but exploit them to learn a domain latent space and map unknown domains with respect to known ones•We show a significant increase in classification accuracy over current state-of-the-art techniques on popular domain generalization benchmarks: PACS, Office-31 and Office-Caltech.